4D Flow Analysis of BAV-Related Fluid-Dynamic Alterations: Evidences of Wall Shear Stress Alterations in Absence of Clinically-Relevant Aortic Anatomical Remodeling

Abstract

Bicuspid aortic valve (BAV) is the most common congenital cardiac disease and is a foremost risk factor for aortopathies. Despite the genetic basis of BAV and of the associated aortopathies, BAV-related alterations in aortic fluid-dynamics, and particularly in wall shear stresses (WSSs), likely play a role in the progression of aortopathy, and may contribute to its pathogenesis. To test whether WSS may trigger aortopathy, in this study we used 4D Flow sequences of phase-contrast cardiac magnetic resonance imaging (CMR) to quantitatively compare the in vivo fluid dynamics in the thoracic aorta of two groups of subjects: (i) five prospectively enrolled young patients with normo-functional BAV and with no aortic dilation and (ii) ten age-matched healthy volunteers. Through the semi-automated processing of 4D Flow data, the aortic bulk flow at peak systole was quantified, and WSSs acting on the endothelium of the ascending aorta were characterized throughout the systolic phase in terms of magnitude and time-dependency through a method recently developed by our group. Variables computed for each BAV patient were compared vs. the corresponding distribution of values obtained for healthy controls. In BAV patients, ascending aorta diameter was measured on cine-CMR images at baseline and at 3-year follow-up. As compared to controls, normo-functional BAV patients were characterized by minor bulk flow disturbances at peak systole. However, they were characterized by evident alterations of WSS distribution and peak values in the ascending aorta. In particular, in four BAV patients, who were characterized by right-left leaflet fusion, WSS peak values exceeded by 27–46% the 90th percentile of the distribution obtained for healthy volunteers. Only in the BAV patient with right-non-coronary leaflet fusion the same threshold was exceeded by 132%. Also, evident alterations in the time-dependency of WSS magnitude and direction were observed. Despite, these fluid-dynamic alterations, no clinically relevant anatomical remodeling was observed in the BAV patients at 3-year follow-up. In light of previous evidence from the literature, our results suggest that WSS alterations may precede the onset of aortopathy and may contribute to its triggering, but WSS-driven anatomical remodeling, if any, is a very slow process.

Bicuspid aortic valve aortopathies: An hemodynamics characterization in dilated aortas

“…Similar findings are present in computational (Bonomi et al 2015) and in vivo studies (Mahadevia et al 2014;Bissell et al 2013), where higher degrees of ascending aortic dilation were associated with lower WSS values. Nonetheless, previous 4D flow studies using nondilated aortas have suggested that abnormally high WSS may anticipate the onset of aortopathy and contribute to its triggering (Piatti et al 2017).…”

Volumetric segmentation‐free method for rapid visualization of vascular wall shear stress using 4D flow MRI

“…Current methods either solely rely on manual segmentation, which comes with implicit user biases and inaccuracies, or with semiautomated methods which expedite the process by averaging frames and/or using splines. [5][6][7][8][9][10][11][12][13][14][15][16][17] While efficient, use of splines is sensitive to a variety of hyperparameters such as the specific position of the nodes. 8 Our proposed algorithm relies solely on empirical data to classify voxels as edge or lumen and is, therefore, both userindependent and inherently reproducible.…”

“…[5][6][7][8][9][10][11][12] In the second approach, trained observers use semiautomatic segmentation packages and interpolation to generate a 3D mesh of the aorta and subsequently evaluate WSS. [13][14][15][16][17] While these are viable methods for calculating WSS on a case-by-case basis, multiple authors note that their primary limitation is the need for user-input for manual segmentation of the aorta. [5][6][7][13][14][15] Specifically, manual segmentation can lead to inter-observer variability for the lumen boundary as well as potential inaccuracies due to the spatiotemporal interpolations involved in 3D mesh generation.…”

“…[13][14][15][16][17] While these are viable methods for calculating WSS on a case-by-case basis, multiple authors note that their primary limitation is the need for user-input for manual segmentation of the aorta. [5][6][7][13][14][15] Specifically, manual segmentation can lead to inter-observer variability for the lumen boundary as well as potential inaccuracies due to the spatiotemporal interpolations involved in 3D mesh generation. An additional limitation is the sheer number of segmentations required to demarcate regions of interest (ROI) throughout the vasculature over time; the aorta is elastic and hence its boundaries temporally, necessitating manual segmentation at every time point.…”

“…An additional limitation is the sheer number of segmentations required to demarcate regions of interest (ROI) throughout the vasculature over time; the aorta is elastic and hence its boundaries temporally, necessitating manual segmentation at every time point. 15 We, therefore, sought to develop an approach to calculate WSS that is less dependent on user input for initial visualization. To validate our method, we compared our results with those obtained using a reference method by Potters et al 8…”